16HK1100, Development of IoT Standard Based Integrated Device Management Platform for Constrained Devices ,
Ko Seok Kap
Abstract
부하분산 장치에서 새 트래픽이 들어 왔을 때 최적의 서버를 선택하여 부하를 분산하기 위해, 기계학습을 기반으로 트래픽 특성과 서버들의 부하 처리 특성을 학습하여 서버를 선택하는 방법이 제공된다. 본 발명의 한 측면에 따르면, 다수의 클라이언트, 실제 요청메시지를 받아서 처리를 수행하고 응답을 전송하는 다수의 처리서버, 그리고 클라이언트들부터 요청메시지를 받아 처리서버에 전달해 주는 부하분산 장치에 있어서, 클라이언트가 요청메시지를 보내면 부하분산을 잘 할 수 있는 처리서버를 기계학습 기반 서버선택부가 선택하는 장치가 제공된다. 상기 서버선택부는 기계학습에 기반하여 새 트래픽에 의한 선택 서버의 부하 증가량 및 처리 지연 시간을 학습하고, 새로운 트래픽이 들어오면, 새 트래픽의 특성과 현재 서버들의 상태를 고려하여 서버별 예상 부하량을 예측하고 그에 따라 가장 적합한 서버를 선택하는 기계학습모듈을 포함한다.
KSP Keywords
Load balancing
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